Optimal control of an M/G/1 queue with impatient priority customers

An optimal operating policy is characterized for the infinite‐horizon average‐cost case of a single server queueing control problem. The server may be turned on at arrival epochs or off at departure epochs. Two classes of customers, each of them arriving according to an independent Poisson processes, are considered. An arriving 1‐customer enters the system if the server is turned on upon his arrival, or if the server is on and idle. In the former case, the 1‐customer is selected for service ahead of those customers waiting in the system; otherwise he leaves the system immediately. 2‐Customers remain in the system until they complete their service requirements. Under a linear cost structure, this paper shows that a stationary optimal policy exists such that either (1) leaves the server on at all times, or (2) turns the server off when the system is empty. In the latter case, we show that the stationary optimal policy is a threshold strategy, this feature being commonplace in most of priority queueing systems and inventory models. However, the optimal policy in our model is determined by two thresholds instead of one. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 201–209, 2001     

The NBUL class of life distribution and replacement policy comparisons

Let X and X_τ denote the lifetime and the residual life at age τ of a system, respectively. X is said to be a NBUL random variable if X_τ is smaller than X in Laplace order, i.e., X_τ ≤_L X. We obtain some characterizations for this class of life distribution by means of the lifetime of a series system and the residual life at random time. We also discuss preservation properties for this class of life distribution under shock models. Finally, under the assumption that the lifetimes have the NBUL property, we make stochastic comparisons between some basic replacement policies. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 578–591, 2001.     

消防办公网络安全需求分析及对策

通过简要介绍消防网络的构成及功能,进行网络安全及需求分析,并结合消防办公信息网的实际恃况,提出制定安全对策的几种建议.     

Optimal policies for M/M/m queue with two different kinds of (N,T)‐policies

In this paper, two different kinds of (N, T)‐policies for an M/M/m queueing system are studied. The system operates only intermittently and is shut down when no customers are present any more. A fixed setup cost of K > 0 is incurred each time the system is reopened. Also, a holding cost of h > 0 per unit time is incurred for each customer present. The two (N, T)‐policies studied for this queueing system with cost structures are as follows: (1) The system is reactivated as soon as N customers are present or the waiting time of the leading customer reaches a predefined time T, and (2) the system is reactivated as soon as N customers are present or the time units after the end of the last busy period reaches a predefined time T. The equations satisfied by the optimal policy (N*, T*) for minimizing the long‐run average cost per unit time in both cases are obtained. Particularly, we obtain the explicit optimal joint policy (N*, T*) and optimal objective value for the case of a single server, the explicit optimal policy N* and optimal objective value for the case of multiple servers when only predefined customers number N is measured, and the explicit optimal policy T* and optimal objective value for the case of multiple servers when only predefined time units T is measured, respectively. These results partly extend (1) the classic N or T policy to a more practical (N, T)‐policy and (2) the conclusions obtained for single server system to a system consisting of m (m ≥ 1) servers. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 240–258, 2000     

Determining a threshold control policy for an imperfect production system with rework jobs

Consider a threshold control policy for an imperfect production system with only a work center handling both regular and rework jobs. An imperfect production system studied here, generates defect jobs by factors other than machine failures. A threshold control or (ω, s) policy sets the guideline for a work center to switch between regular and rework jobs. A production cycle begins with loading and processing of several batches of regular jobs with a lot size equal to s. The outcome of each completed regular job is an independent Bernoulli trial with three possibilities: good, rework, or scrap. Once the work center accumulates more than a threshold ω of rework jobs, it finishes the last batch of regular jobs and switches to rework jobs. The objective of this research is to find a threshold ω and a lot size s that maximize the average long‐term profit. The ultimate goal is to construct a simple algorithm to search for ω and s that can be implemented directly in production management systems, as a result of this work. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 273–301, 1999     

军用安全模型扩展及其应用

在分析基于格理论的信息流控制军用安全模型在实际应用中存在的不足的基础上,从安全类定义和信息流控制策略两个方面对原模型进行了理论扩展和安全性分析.扩展模型安全类在作为对象和主体时具有相同的表达方式,以及在保持原模型信息流关系情况下,允许同职权等级用户交流他们共知的信息,以及高职权等级用户访问低职权等级用户他们之间共知的信息.以数字文档安全管理系统为例对扩展模型应用进行了描述.实例表明扩展模型更适合实际系统对信息流控制的要求.     

对高等教育若干问题的思考

目前高等教育中存在的种种弊端,迫使高等教育必须改革。高等教育决不能脱离党的教育方针,要强化世界观和价值观教育,远离社会不良风气的干扰,培养出德智体全面发展的人才。     

基于失效树的某型无坐力炮弹储存可靠性分析

通过对某型无坐力炮弹战斗部的结构分析和功能分析,以弹着目标不爆炸为顶事件建立了失效树,并根据该型弹药的功能构件在储存期间的特点选择了失效树的简化原则,建立了系统可靠度模型。确定了由储存因素导致弹着目标不爆炸这一事件的4种主要失效模式。并通过分析其不同的储存期失效机理和影响因素,提出该型炮弹的储存策略。     

动态灰色时序系统 DGTS 及其在安全策略规范中的应用

设计安全策略模型时应考虑安全策略的时序特性和灰色特性,还应综合考虑保密性、完整性和可用性三方面的需求。已有的安全模型没有描述时序性和灰色特性,而且一般只侧重描述保密性或完整性。为此,本文提出了动态灰色时序系统（简称ＤＧＴＳ）。本文详细描述了ＤＧＴＳ,以及基于ＤＧＴＳ的动态语义设计的灰色时序安全策略规范语言ＧＴＳＬ。     

RETROSPECT AND PROSPECT: TRENDS IN COST AND THEIR IMPLICATIONS FOR UK AEROSPACE

This paper, intended to stimulate debate, suggests that we are at a turning point in the history of the UK military aerospace industry. It argues that there is urgent need for a fresh vision as to how the UK military aerospace industry can both prosper and make its maximum contribution to the defence of Britain. Further, the resulting plans and policies must emphasise the robustness of defence capabilities in a future in which the only thing we can be certain is that it will be very different from the present – especially whenever defence most matters.